Data Science Interview Questions | Data Science Interview Questions Answers And Tips | Simplilearn
58. Difference between an error and a residual error
The difference between a residual error and error are defined below –
Error |
Residual Error |
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The difference between the actual value and the predicted value is called an error. Some of the popular means of calculating data science errors are –
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The difference between the arithmetic mean of a group of values and the observed group of values is called a residual error. |
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An error is generally unobservable. |
 A residual error can be represented using a graph. |
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A residual error is used to show how the sample population data and the observed data differ from each other. |
 An error is how actual population data and observed data differ from each other. |
17. What is logistic regression? State an example where you have recently used logistic regression.
Logistic Regression is also known as the logit model. It is a technique to predict the binary outcome from a linear combination of variables (called the predictor variables).
For example, let us say that we want to predict the outcome of elections for a particular political leader. So, we want to find out whether this leader is going to win the election or not. So, the result is binary i.e. win (1) or loss (0). However, the input is a combination of linear variables like the money spent on advertising, the past work done by the leader and the party, etc.
Why Did You Opt for a Data Science Career?
Tell them how you got passionate about data science. You can share a quick story or talk about a specific area that served as your gateway to data science, such as statistical analysis or Python programming.
Then, talk about your background—your college degree, previous companies you’ve worked at, and data science courses that you’ve completed.
Finally, relate your interests to the organization’s needs, and explain how your expertise in data science can help the company solve its challenges.
A statistical interaction is when two or more variables interact, and this results in a third variable being affected.
Linear regression is a tool for quick predictive analysis. For example, the price of a house depends on a myriad of factors, including its size and location. In order to see the relationship between these variables, you can build a linear regression, which predicts the line of best fit and can help conclude whether or not these two factors have a positive or negative relationship.
51. What is better – random forest or multiple decision trees?
Random forest is better than multiple decision trees as random forests are much more robust, accurate, and lesser prone to overfitting as it is an ensemble method that ensures multiple weak decision trees learn strongly.
